基于驾驶意图识别与行驶工况识别的地下矿车控制策略

发布时间：2018-06-18 02:06

本文选题：地下矿车 + 电传动控制策略　；参考：《北京科技大学》2015年博士论文

【摘要】：随着交通运输业的发展,汽车保有量快速增加,世界正面临能源紧张和排放污染的紧迫局势,各国纷纷致力于“节能减排”的研究。目前的研究已经从减小乘用车、轻型车的燃油消耗开始,扩大到非公路重型车辆燃油经济性的提高上。电传动矿用自卸车是矿山运输中重要的运输工具,其运载能力强单车燃油消耗量大,在矿石开采成本中燃油成本占很大比重,因此针对矿用汽车燃油经济性的研究具有重要意义。电传动系统是电传动矿用自卸车运行的核心机构,在牵引工况控制能量从柴油机、发电机传递至轮边牵引电机,在制动工况利用制动电阻将轮边牵引电机再生发电的制动能量以热的形式消耗掉。因此电传动系统性能极大地影响了地下矿车的动力性、平顺性和燃油经济性。本文针对地下矿用自卸车的电传动系统控制策略进行研究以提高地下矿车的燃油经济性。首先从人-车-路闭环的角度对地下矿车传统恒功率控制算法深入分析,探究影响车辆燃油经济性的因素。在此基础上提出一种电传动控制策略,对驾驶意图和车辆行驶工况进行识别,综合驾驶员需求和行驶工况对电传动系统功率工作点进行决策,以达到在保障动力性前提下提高燃油经济性的目的。运用模糊识别方法对驾驶员加速意图、制动意图及平稳行驶意图进行识别,并应用实际工况统计数据指导识别参数隶属度函数的确定,提高模糊识别的准确性。基于目前尚无地下矿车行驶工况研究的情况,对地下矿车行驶工况分类进行了研究,依托实际工况数据,运用统计学K-means聚类方法进行工况分析,得到带有车辆驱动功率特征信息的工况分类。应用LVQ神经网络技术,对所建立的4种行驶工况进行识别。最后在恒功率控制策略架构基础之上,提出了基于驾驶意图和行驶工况的地下矿车电传动控制策略。在Maplesim/Simulink环境下研发了电传动地下矿车前向仿真平台,进行多工况仿真实验。结果表明,相较于恒功率控制策略,本文提出的控制策略提高了地下矿车的工况适应性和燃油经济性。
[Abstract]:With the development of transportation and the rapid increase of vehicle ownership, the world is facing the urgent situation of energy stress and emission pollution. Many countries are devoting themselves to the research of "energy saving and emission reduction". The current research has begun with reducing the fuel consumption of passenger vehicles and light vehicles, and has been expanded to improve the fuel economy of off-road heavy vehicles. Electric drive mine dump truck is an important transport vehicle in mine transportation, and its transportation capacity is strong, the fuel consumption of single vehicle is large, and the fuel cost accounts for a large proportion in the mining cost of ore. Therefore, it is of great significance to study the fuel economy of mining vehicles. The electric drive system is the core mechanism of the electric drive mine dump truck. In the traction condition, the control energy is transferred from the diesel engine to the generator to the wheel-side traction motor. The braking energy of regenerative generation of wheel-side traction motor is consumed in the form of heat by using brake resistance in braking condition. Therefore, the performance of electric transmission system greatly affects the power performance, ride comfort and fuel economy of underground mining vehicle. In this paper, the electric drive control strategy of underground dump truck is studied to improve the fuel economy of underground dump truck. Firstly, the traditional constant power control algorithm of underground mining vehicle is deeply analyzed from the angle of man-vehicle-road closed loop, and the factors that affect vehicle fuel economy are explored. On this basis, an electric drive control strategy is proposed to identify the driving intention and vehicle driving conditions, and to make decision on the power working point of the electric drive system by synthesizing the driver's requirements and driving conditions. In order to achieve the purpose of improving fuel economy under the premise of ensuring power performance. The fuzzy identification method is used to identify the driver's acceleration intention, braking intention and steady driving intention, and the statistical data of actual working conditions are used to guide the determination of membership function of the identification parameters to improve the accuracy of fuzzy recognition. Based on the fact that there is no research on the driving conditions of underground mining vehicles, the classification of driving conditions of underground mining vehicles is studied. Based on the actual working condition data, the working conditions are analyzed by K-means clustering method of statistics. The working condition classification with the characteristic information of vehicle driving power is obtained. LVQ neural network technology is used to identify the four driving conditions. Finally, based on the structure of constant power control strategy, the electric drive control strategy of underground mining vehicle based on driving intention and driving condition is proposed. In the environment of Maplesimr / Simulink, the forward simulation platform of electric drive underground mining vehicle is developed, and the simulation experiment is carried out under multiple working conditions. The results show that compared with the constant power control strategy, the proposed control strategy improves the operating adaptability and fuel economy of underground mining vehicles.
【学位授予单位】：北京科技大学
【学位级别】：博士
【学位授予年份】：2015
【分类号】：TD634

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